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MeLOn
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Typedefs | |
using | X = lhsdesign(nX,DX) |
Functions | |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University Xiaopeng Daniel and Alexander Mitsos date January clc clear all close all | addpath ("Direct") |
Add path for DIRECT solver | addpath ("GP training") |
Add path for Gaussian process training functions | addpath ("Write GP to files") |
Evaluate test_func for all X Tranining of GPs Opt | GP (1).matern |
id | GP_Parameters () |
Write_GP_to_json (join([filename, ".json"]), Opt.GP(1), X, Y, lb, ub) | |
Compute GP predictions in | Matlab (just for information) x_Test_Point |
Plot generated | data (just for information) % Generate a mesh on the inputs[x_1_prediction |
y_prediction (i, j) | |
y_std (i, j) | |
Variables | |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in | Matlab |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University | n |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University Xiaopeng | Lin |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University Xiaopeng Daniel | Grothe |
Add path for Gaussian process export functions General | nX = 20 |
Number of training data points | DX = 2 |
Input dimension of data GP | lb = [-3, -3] |
Define Lower bound of inputs | ub = [ 3, 3] |
Define upper bound of inputs | test_func |
Scale inputs onto interval[lb, ub] | Y = cellfun(test_func, num2cell(X,2)) |
Define covariance function | Martern |
internal option for solver | Opt = Train_GP_and_return_hyperparameters(X,Y,lb,ub,Opt) |
Plot generated | x_2_prediction = meshgrid(linspace(lb(1), ub(1), 100), linspace(lb(2), ub(2),100) ) |
y_prediction = zeros(size(x_2_prediction,1),1) | |
y_std = zeros(size(x_2_prediction,1),1) | |
Evaluate prediction at all mesh points for | i |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University Xiaopeng Daniel and Alexander Mitsos date January clc clear all close all addpath | ( | "Direct" | ) |
Plot generated data | ( | just for | information | ) |
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virtual |
Compute GP predictions in Matlab | ( | just for | information | ) |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University Xiaopeng Daniel Grothe |
Evaluate prediction at all mesh points for i |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University Xiaopeng Lin |
Define covariance function Martern |
file example_training_of_GP m brief Illustrative training script for Gaussian processes in RWTH Aachen University n |
Add path for Gaussian process export functions General nX = 20 |
Define upper bound of inputs test_func |
Define Lower bound of inputs ub = [ 3, 3] |
y_prediction = zeros(size(x_2_prediction,1),1) |
y_std = zeros(size(x_2_prediction,1),1) |